Article
Design Hybrid Iterative Learning Controller for Directly
Driving the Wheels of Mobile Platform against Uncertain
Parameters and Initial Errors
Lijun Qiao
1
, Luo Xiao
2,
* , Qingsheng Luo
1
, Minghao Li
3
and Jianfeng Jiang
1
Citation: Qiao, L.; Xiao, L.; Luo, Q.;
Li, M.; Jiang, J. Design Hybrid
Iterative Learning Controller for
Directly Driving the Wheels of
Mobile Platform against Uncertain
Parameters and Initial Errors. Appl.
Sci. 2021, 11, 8181. https://doi.org/
10.3390/app11178181
Academic Editor: Giovanni Boschetti
Received: 10 August 2021
Accepted: 30 August 2021
Published: 3 September 2021
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4.0/).
1
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China;
Qiao_Lijun_123@163.com (L.Q.); luoqsh@bit.edu.cn (Q.L.); xiangyang_ersheng@163.com (J.J.)
2
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
3
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China; swd_16@163.com
* Correspondence: luox@bit.edu.cn
Abstract:
In this paper, we develop a hybrid iterative learning controller (HILC) for a non-holonomic
wheeled mobile platform to achieve trajectory tracking with actual complex constraints, such as
physical constraints, uncertain parameters, and initial errors. Unlike the traditional iterative learning
controller (ILC), the control variable selects the rotation speed of two driving wheels instead of
the forward speed and the rotation speed. The hybrid controller considers the physical constraints
of the robot’s motors and can effectively handle the uncertain parameters and initial errors of the
system. Without the initial errors, the hybrid controller can improve the convergence speed for
trajectory tracking by adding other types of error signals; otherwise, the hybrid controller achieves
trajectory tracking by designing a signal compensation for the initial errors. Then, the effectiveness
of the proposed hybrid controller is proven by the relationship between the input, output, and status
signals. Finally, the simulations demonstrate that the proposed hybrid iterative learning controller
effectively tracked various trajectories by directly controlling the two driving wheels under various
constraints. Furthermore, the results show that the controller did not significantly depend on the
system’s structural parameters.
Keywords:
non-holonomic wheeled mobile platform; hybrid iterative learning controller; trajectory
tracking; uncertain parameters; initial errors
1. Introduction
Due to the simple structure design and strong movement ability, mobile platforms
have been extensively applied in various fields, including agricultural production [
1
],
environmental detection [
2
], and industrial production [
3
]. Due to the differences in the
number of motors and the structure shape, differential mobile robots controlled by two
driving wheels have become the mainstream mobile robot. In the movement of the mobile
platform, there are two actual input control variables: the speeds of the driving wheels on
the left side and the right side [4].
However, the actual positioning of the mobile platform requires three parameters: the
position of the
xy
plane and the rotation angle of the
z
axis [
5
]. Therefore, a differential
mobile robot with two driving wheels is a typical non-holonomic constraint system [
6
].
In solving various problems associated with this mobile robot, trajectory tracking is an
important research problem with a high application value in many fields.
With mobile robots, uncertain parameters are unavoidable, which are caused by
many factors, such as physical error in the machining [
7
], external disturbances in the
environment [
8
], equipment wear during working [
9
], and disturbances in the control
signals [10]
. To deal with these uncertainties parameters, various controllers have been pro-
posed, such as the back-stepping controller [
11
], model predictive controller [
12
,
13
], sliding
Appl. Sci. 2021, 11, 8181. https://doi.org/10.3390/app11178181 https://www.mdpi.com/journal/applsci
